Coastal water quality estimation from Geostationary Ocean Color Imager (GOCI) satellite data using machine learning approaches

Abstract

It is important to monitor coastal water quality using key parameters such as chlorophyll-a concentration and suspended sediment to better manage coastal areas as well as to better understand the natre of biophysical processes in coastal seawater. Remote sensing technology has been commonly used to monitor coastal water quality due to its ability of covering vast areas at high temporal resolution. While it is relatively straightforward to estimate water quality in open ocean (i.e. Case I water) using remote sensing, coastal water quality estimation is still challenging as many factors can influence water quality, including various materials coming from inland water systems and tidal circulation. There are continued efforts to accurately estimate water quality parameters in coastal seawater from remote sensing data in a timely manner.In this study, two major water quality indicators, chlorophyll-a concentration and the amount of suspended sediment, were estimated using Geostationary Ocean Color Imager (GOCI) satellite data. GOCI, launched in June 2010, is the first geostationary ocean color observation satellite in the world. GOCI collects data hourly for 8 hours a day at 6 visible and 2 near-infrared bands at a 500 m resolution with 2,500 x 2,500 km square around Korean peninsula. Along with conventional statistical methods (i.e. various linear and non-linear regression), three machine learning approaches such as rawater. Remote sensing technology has been commonly used to monitor coastal water quality due to its ability of covering vast areas at high temporal resolution. While it is relatively straightforward to estimate water quality in open ocean (i.e. Case I water) using remote sensing, coastal water quality estimation is still challenging as many factors can influence water quality, including various materials coming from inland water systems and tidal circulation. There are continued efforts to accurately estimate water quality parameters in coastal seawater from remote sensing data in a timely manner.In this study, two major water quality indicators, chlorophyll-a concentration and the amount of suspended sediment, were estimated using Geostationary Ocean Color Imager (GOCI) satellite data. GOCI, launched in June 2010, is the first geostationary ocean color observation satellite in the world. GOCI collects data hourly for 8 hours a day at 6 visible and 2 near-infrared bands at a 500 m resolution with 2,500 x 2,500 km square around Korean peninsula. Along with conventional statistical methods (i.e. various linear and non-linear regression), three machine learning approaches such as r